Serverless Microservice: Conclusion

Reference: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6

Over the last 6 parts of this series we have delved into the planning an implementation of a serverless microservice. Our example, while simple, does show how serverless can ease certain pain points of the process. That is not to say Serverless is without its own pain points, the limited execution time being one of the largest. Nevertheless, I see serverless as an integral part of any API implementation that uses the Cloud.

The main reason I would want to use Serverless is due to the integration with the existing Cloud platform. There are ways, of coursem to write these operations into an existing implementation, however, such a task is not going to be a single line and an attribute. I also believe that the API Manager concept is very important; we are already starting to see its usage within our projects at West Monroe.

The API management aspect gives you greater control over versioning and redirection. It allows you to completely change an endpoint without the user seeing any changes to it. Its a very powerful tool and I look forward to many presentations and blog entries on it in the future.

Returning to the conversation on serverless, the prevailing question I often hear is around cost: how much more or less do I pay with serverless vs a traditional approach that might be stand alone services or through something like Docker and/or Kubernetes.


Bearing in mind that you are automatically granted, monthly, 1 million serverless executions per month ($0.20 per million after that). There is also a cost for the execution time. The example that Microsoft lays out is a function that uses 512MB of memory and executes 3,000,000 times (4,000,000 considering the free million) would cost about $18 a month.

On the whole I feel this is a cost savings over something like App Service, though it would depend on what you are doing. As to how it compares to AWS Lambda, it is more or less equivalent, Azure might be cheaper, but the difference is slight at best.

Closing Thoughts

As I said above, I see a lot of value in using Serverless over traditional programming, especially as more and more backends are adopting event driven architectures. Within this space Serverless is a very attractive because of the each with which it integrates and listens to the existing cloud infrastructure.

I would be remiss if I didnt call out the main disadvantages of Serverless. While there is work going on at Microsoft, and I assume Amazon, to reduce the cold startup time, it remains an issue. That plus the limited execution time (5m) mean that developers must have a solid understanding of their use case before they use Serverless; a big reason why I started this series with a Planning post.

The one other complaint I have heard is more of a managerial notion which is, when I create a traditional application in Visual Studio, its associated with a solution and project and its very clear what is associated with what. With serverless, it feels like a bunch of loose functions lacking any organization.

From what I have seen in AWS this is a fair complaint, AWS Lambda functions are just a list of functions that one would need to understand what is what and how it is used; not ideal. In Azure, however, you can logically organize things such that relevant functions are grouped together; Visual Studio Serverless tools make this very easy. In my view, on Azure, this complaint does not hold water, on AWS, based on what I know this is a difficulty.

Outside of these drawbacks the other area I am focusing on is how to work DevOps into a serverless process. While both Lambda and Azure Functions offer the ability to debug locally (Azure is better IMO) the process of publishing to different environments needs more work.

The topic of Serverless is one I am very keen on in both AWS and Azure. I plan to have quite a few posts in the future directed at this, in particular some of the more advanced features supporting the development of APIs within AWS and Azure.


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